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Month: October 2015

Welcome to the Arclight Guide! This guide will help you get started by introducing you to the basic functions and options Arclight offers.

Performing a Search

Performing a search in Arclight is easy and fast. In the query box in the top left corner, simply enter an query and hit enter. The resulting graph will show how your query trends across the Media History Digital Library. By moving your cursor over the data points, Arclight will tell you how many page hits your query had in that particular year. Additionally, when you click the data point, it will take you to Lantern so you can have a closer look at these specific pages.

Performing a Multiple Query Search

Arclight isn’t limited to one query. If I wanted to search for the cast of GrandHotel (1932), I can take a list and copy and paste the queries into the search box and hit enter. Note that if you are using a word processor, each query on the list should be on a new line.

When performing searches with several queries, the results may be overwhelming. On the left side of the screen will be tabs with each query. When you move your cursor over a tab, it gives you some options. The large X removes the query from search, while Eye button toggles the visibility of the query on and off.

Setting a Year Range

This button will allow you to set date limits on your searches. When you click this button, a tab will appear where you can enter the years you want to focus on. When the years limits are set, the graph will adjust and scale to your time period.

The Maximum Hit Guide

This button will add the Maximum Hits Guideline to your graph. This guideline tells you the total number of pages the Media History Digital Library has for each year. This guideline reveals some important information, such as gaps in our corpus (e.g. 1901-1902) and years where we have the most pages. With this, we can make better interpretations of our results (see Normalizing Data below for more).

Normalizing Data

This button will take the number of page hits of each year and divides it by the total number of pages available in that year (the Maximum Hits Guideline) to return a percentage. This is helpful because the resulting graph now shows the results on a common scale.

For example, let’s compare two graphs about Greta Garbo. This graph shows the number of pages hits per year during the time period from 1925 to 1935. Notice how Greta Garbo peaks in 1929 with over 800 page hits.

The graph below shows the same search, except the data is normalized. Instead of page hits, the graph refers to percentages. In 1929, Greta Garbo was registered in about 2.3% of the Media History Digital Library‘s pages for that year. Notice how in this graph, her results peak in 1932; while her overall page hits were relatively smaller in 1932, she was in a higher percentage of the available pages. This could be due to several reasons: increasing or waning popularity, changing marketing practices, the varying makeup of the corpus between years, and/or simple mathematical statistics as 1932 has over 10,000 fewer pages than 1929. While this warrants further investigation, the main point is that we should consider larger contexts when looking at our results and normalizing our data helps us with this.

Stack Series

This button will allow you to combine or “stack” queries so that you can see how they trend together. Starting at the front with the last query entered, Arclight takes those numbers and adds it to the next layer behind, and repeats until it reaches the first query entered.

In the graph below, we have a search for Greta Garbo and Joan Crawford (in that order). In the front layer, the pink Joan Crawford, Arclight is giving her basic results. In the orange layer, Arclight is combining both numbers. In this case, Garbo and Crawford had over 1,500 combined hits in 1930.

For more than two entries, the process continues until it reaches the back layer. In the graph below, Yellow is John Barrymore, Pink is Barrymore and Joan Crawford, and Orange is Barrymore, Crawford, and Greta Garbo.

Journal Filter

This button allows you to limit your search to specific journals. Clicking the button will produce a menu that will allow you to search and select which journal(s) you wish to search. Once selected, the journal title will appear on the right side. You can remove journals by clicking the X next to their titles. Once you are done, click outside the menu and Arclight will automatically perform the searches.

Exporting Data

After you have completed your searches, Arclight lets you download the graphs and data in multiple formats. Clicking this button will bring up a menu where you can select your preferred format:

We hope this Introduction Guide has been helpful and if you have any further questions, don’t hesitate to contact us here.

Project Arclight began two years ago with an idea. If researchers can use Twitter analytics to study trends in discussions of contemporary media, then what if we treated historic trade papers and fan magazines like a giant Twitter stream and explored trends in film and media history? We worked on refining this idea, received a grant, kept working on it, and — just today! — publicly launched our software at http://search.projectarclight.org.

Arclight searches the nearly 2 million page collection of the Media History Digital Library (MHDL) and graphs the results. To provide one example — and an example very much inspired by this month’s baseball postseason, football season, and basketball preseason — here is a visualization of how those three sports trend across the MHDL corpus. Note: Because the MHDL’s collections primarily encompass out-of-copyright works, the results largely cut off after the year of 1964.

The team that developed the Arclight software — who are acknowledged at the end of this post — are working on a series of journal articles that model how Arclight and the method of Scaled Entity Search can be applied toward investigating large-scale research questions. However, we also hope that Arclight will be valuable as a classroom tool for teachers of film and broadcasting history, especially those teachers keen to expose students to digital humanities methodologies and engage them in active learning. Here are a couple of suggestions for film and media educators about how to use Arclight with your students.

The POE Strategy

For over twenty years, the POE strategy (which stands for Predict, Observe, Explain) has been a highly effective teaching method in the sciences. After being presented with a set of circumstances, students are asked to predict what will happen, observe an experiment, and then explain why it happened and compare their prediction to the outcome. When implemented well, it’s an exercise that actively engages 100% of students in the classroom — not just the two or three who might raise their hands to answer a question that the teacher asks aloud.

The POE strategy is ideal when used with science experiments that play out relatively quickly. What will happen when we mix these two chemicals together? Make your prediction, then observe and explain why the result occurred. But POE can be challenging to implement in a history classroom. If a teacher says, “and guess what happened next?,” then it can certainly facilitate student prediction. But it also perpetuates two unfortunate dynamics. First, the instructor, who reveals the correct answer, becomes reinforced as the authority on the historical record. Second, history is presented as a fixed narrative, rather than as a set of assumptions and arguments that we are always challenging using the available evidence. We need ways to more actively engage students in their learning. And active learning, in a film and media history classroom, means that students get to spend class time doing the work of a film and media historian.

Arclight offers one means of integrating POE and active learning into a film or media history classroom. To use my earlier graph example, a teacher might ask, “How did the discourse of sports change from 1900 to 1960 in books and magazines about American entertainment and media?” Students could write down their predictions, then get to work on their computers or phones running queries for baseball, basketball, football, and other terms in Arclight. Something might immediately jump out at them. For me, it was the decline of both baseball and football during the years of 1942, 1943, 1944, and 1945. Based on this observation, I would offer the explanation that this decline of baseball and football occurred due to the impact of World War II and the enlistment of athletes into armed forces.

But really, this explanation based on distant reading is a new prediction that invites closer inspection, observation, and analysis. Put another way, Arclight is best used with the POEPOE or POEPOEPOE strategy. Any explanation a student offers can and should be further tested. Does the rise of football in the late-1920s and 1930s have more to do with radio coverage or football’s popularity in short films? We need to dig deeper to find the answer.

In developing Arclight, we felt it was important to give users the ability to easily and fluidly access the underlying texts. We were able to achieve this by integrating Arclight with the MHDL’s search engine, Lantern. Students can click through the Arclight graph and access the underlying materials within Lantern. Teachers will also want to encourage students to consult primary sources that are NOT indexed within Lantern, like archival manuscript collections and historical newspapers. Still, Arclight and Lantern provide a fast, user-friendly way for students to actively engage in historical research and analysis within a classroom.

The SES Interpretive Triangle and Changing Graph Views

There is another interpretive method that teachers may want to consider alongside POE.

The line graphs in Arclight are not arguments. They are simply visualizations of how many MHDL pages a given term appears in per year. To help students more fully think through what they are seeing, teachers might ask them to think about the relationships between the terms they are searching, the books and magazines they are searching within, and the impact of digitization and search algorithms within the process. We call this the Scaled Entity Search Interpretative Triangle. And if this sounds big and confusing, see Kit Hughes’ blog posts on the Scaled Entity Search (SES) technical method and interpretive method.

On an interactive level, students can change their visualization and reflective on the corpus and digitization by clicking on the dotted line icon and/or percentage icon. The dotted line icon graphs the MHDL’s entire corpus, revealing how some years have way more pages indexed than other years. The percentage icon helps correct for this by “normalizing” the data that is being visualized by dividing the number of page hits per year by the number of total pages per year. In other words, the normalization feature accounts for the fact that more pages were scanned from some years than others. And in the case of the sports visualization, the trend lines change quite a bit — especially in the stability of the lines from the late-1940s through the mid-1950s:

Ultimately, no visualization is perfect, nor should it be. By offering users a variety of visualization options and the ability to access the underlying data, we hope to drive home the understanding that all graphs are incomplete abstractions and best used as jumping off points into further analysis. Yet they are valuable precisely because they may lead us toward analyses and questions that we otherwise would have never considered. And we hope that you and your students may even have fun creating, changing, and playing with these visualizations.

Please give Arclight a try with your students and let us know how it goes. We hope that it allows students to playfully engage in historical exploration and come away with valuable lessons about digital technology too. We are all living in a big data world. We have long trained our students in how to closely read a singular text. We need to complement this with more teaching activities that encourage analyzing many texts at a large scale — and dealing with all the uncertainty and messiness that goes along with it.

Acknowledgements:

This project was developed by teams at the University of Wisconsin-Madison and Concordia University and sponsored by a Digging into Data grant from the U.S.’s Institute for Museum and Library Services and Canada’s Social Sciences and Humanities Research Council. Additional support came from the University of Wisconsin-Madison’s Office of the Vice Chancellor for Research and Graduate Education and Concordia University’s Media History Research Centre.

Thank you also to broader team and community who contributed to Project Arclight.

One final thanks…

The author wishes to thank the Madison Teaching and Learning Excellence (MTLE) program. Without MTLE, this media scholar would never have learned about POE or adopted the strategies of active learning.

Project Arclight and the Media History Research Centre invites you to a Project Arclight talk

JOHANNA DRUCKER

DIGITAL HUMANITIES: FROM SPECULATIVE TO SKEPTICAL

The challenge to Digital Humanities as a field is whether or not any of this activity has had an intellectual impact on any specific field or discipline. This skeptical talk looks at the methodological foundations of Digital Humanities, its development, and accomplishments, but poses a series of questions about what the future should or might look like, and whether there is an intellectual future for this field.

Johanna Drucker is the Breslauer Professor of Bibliographical Studies in the Department of Information Studies at UCLA.